ReinforcedQuickieHyperStrategy

# --- Do not remove these libs ----------------------------------------------------------------------------------------
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import logging
from pandas import DataFrame, DatetimeIndex, merge
from freqtrade.strategy import IStrategy
from typing import Dict, List
from skopt.space import Dimension, Real
# ---------------------------------------------------------------------------------------------------------------------

# --- logger for parameter merging output, only remove if you remove it further down too! -----------------------------
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------------------------------------------------


class ReinforcedQuickieHyperStrategy(IStrategy):
    """
    enhanced auto hyperoptable version based on
    https://github.com/back8/github_freqtrade_freqtrade-strategies/blob/master/user_data/strategies/berlinguyinca/ReinforcedQuickie.py

    !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    !!! as of today (14.04.2021) you need the freqtrade/develop version to be able     !!!
    !!! to run hyperopt/backtest with this new strategy format                         !!!
    !!!                                                                                !!!
    !!! please check https://github.com/freqtrade/freqtrade/pull/4596 for further      !!!
    !!! information about the new auto-hyperoptable strategies!                        !!!
    !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    
    original author@: Gert Wohlgemuth

    works on new objectify branch!

    idea:
        only buy on an upward tending market
   """

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "0": 0.01
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.05

    # Optimal timeframe for the strategy
    timeframe = '5m'

    # resample factor to establish our general trend. Basically don't buy if a trend is not given
    resample_factor = 12

    # default buy parameters (not used in this strategy)
    buy_params = { }

    # default sell parameters (not used in this strategy)
    sell_params = { } 

    EMA_SHORT_TERM = 5
    EMA_MEDIUM_TERM = 12
    EMA_LONG_TERM = 21

    def __init__(self, config: dict) -> None:
        super().__init__(config)
        try:
            from mergedeep import merge
        except ImportError as error:
            # Output expected ImportErrors.
            logger.info("could not import mergedeep, please check if pip is installed: %s", error)
            logger.info("therefor we are not able to merge parameters from config")
        else:
            logger.info('mergedeep found, so attempting to find strategy parameters in config file')
            if self.config.get('strategy_parameters', {}).get(self.__class__.__name__, False):
                cfg_strategy_parameters = self.config.get('strategy_parameters', {}).get(self.__class__.__name__, False)
                logger.info('strategy_parameters from config: %s', repr(cfg_strategy_parameters))
                if cfg_strategy_parameters.get('buy_params', {}):
                    logger.info('merging buy_params from config: %s', cfg_strategy_parameters.get('buy_params'))
                    merge(self.buy_params, cfg_strategy_parameters.get('buy_params'))
                if cfg_strategy_parameters.get('sell_params', {}):
                    logger.info('merging sell_params from config: %s', cfg_strategy_parameters.get('sell_params'))
                    merge(self.sell_params, cfg_strategy_parameters.get('sell_params'))
            else:
                logger.info('no strategy_parameters found in config')
            logger.info('final buy_params: %s', repr(self.buy_params))
            logger.info('final sell_params: %s', repr(self.sell_params))


    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = self.resample(dataframe, self.timeframe, self.resample_factor)

        ##################################################################################
        # buy and sell indicators

        dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_SHORT_TERM
        )
        dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_MEDIUM_TERM
        )
        dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_LONG_TERM
        )

        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=20, stds=2
        )
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']

        dataframe['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
        dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)

        dataframe['cci'] = ta.CCI(dataframe)
        dataframe['mfi'] = ta.MFI(dataframe)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)

        dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4

        ##################################################################################
        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_middleband'] = bollinger['mid']

        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                    (
                            (
                                    (dataframe['close'] < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
                                    (dataframe['close'] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
                                    (dataframe['close'] == dataframe['min']) &
                                    (dataframe['close'] <= dataframe['bb_lowerband'])
                            )
                            |
                            # simple v bottom shape (lopsided to the left to increase reactivity)
                            # which has to be below a very slow average
                            # this pattern only catches a few, but normally very good buy points
                            (
                                    (dataframe['average'].shift(5) > dataframe['average'].shift(4))
                                    & (dataframe['average'].shift(4) > dataframe['average'].shift(3))
                                    & (dataframe['average'].shift(3) > dataframe['average'].shift(2))
                                    & (dataframe['average'].shift(2) > dataframe['average'].shift(1))
                                    & (dataframe['average'].shift(1) < dataframe['average'].shift(0))
                                    & (dataframe['low'].shift(1) < dataframe['bb_middleband'])
                                    & (dataframe['cci'].shift(1) < -100)
                                    & (dataframe['rsi'].shift(1) < 30)
                                    & (dataframe['mfi'].shift(1) < 30)

                            )
                    )
                    # safeguard against down trending markets and a pump and dump
                    &
                    (
                            (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) &
                            (dataframe['resample_sma'] < dataframe['close']) &
                            (dataframe['resample_sma'].shift(1) < dataframe['resample_sma'])
                    )
            )
            ,
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                    (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
                    (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
                    (dataframe['close'] >= dataframe['max']) &
                    (dataframe['close'] >= dataframe['bb_upperband']) &
                    (dataframe['mfi'] > 80)
            ) |

            # always sell on eight green candles
            # with a high rsi
            (
                    (dataframe['open'] < dataframe['close']) &
                    (dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
                    (dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
                    (dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
                    (dataframe['open'].shift(4) < dataframe['close'].shift(4)) &
                    (dataframe['open'].shift(5) < dataframe['close'].shift(5)) &
                    (dataframe['open'].shift(6) < dataframe['close'].shift(6)) &
                    (dataframe['open'].shift(7) < dataframe['close'].shift(7)) &
                    (dataframe['rsi'] > 70)
            )
            ,
            'sell'
        ] = 1
        return dataframe

    def resample(self, dataframe, interval, factor):
        # defines the reinforcement logic
        # resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
        df = dataframe.copy()
        df = df.set_index(DatetimeIndex(df['date']))
        ohlc_dict = {
            'open': 'first',
            'high': 'max',
            'low': 'min',
            'close': 'last'
        }
        df = df.resample(str(int(interval[:-1]) * factor) + 'min',
                         label="right").agg(ohlc_dict).dropna(how='any')
        df['resample_sma'] = ta.SMA(df, timeperiod=25, price='close')
        df = df.drop(columns=['open', 'high', 'low', 'close'])
        df = df.resample(interval[:-1] + 'min')
        df = df.interpolate(method='time')
        df['date'] = df.index
        df.index = range(len(df))
        dataframe = merge(dataframe, df, on='date', how='left')
        return dataframe

    # nested hyperopt class    
    class HyperOpt:
        # defining buy / sell indicator spaces as dummy, 
        # so that no error is thrown about missing sell indicators
        # when hyperopting for all spaces
        @staticmethod
        def indicator_space() -> List[Dimension]:
            return []

        @staticmethod
        def sell_indicator_space() -> List[Dimension]:
            return []
        
        # custom stop loss range
        @staticmethod
        def stoploss_space() -> List[Dimension]:
            return [
                Real(-0.05, -0.02, name='stoploss'),
            ]